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Basic research on biodiversity has concentrated on individual species—naming new species, studying distribution patterns, and analyzing their evolutionary relationships. Yet biodiversity is more than a collection of individual species; it is the combination of biological entities and processes that support life on Earth. To understand biodiversity we must catalog it, but we must also assess the ways species interact with other species to provide functional support for the Tree of Life. Ecological interactions may be lost well before the species involved in those interactions go extinct; their ecological functions disappear even though they remain. Here, I address the challenges in studying the functional aspects of species interactions and how basic research is helping us address the fast-paced extinction of species due to human activities.

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Faced with effectively unlimited choices of how to spend their time, humans are constantly balancing a trade-off between exploitation of familiar places and exploration of new locations. Previous analyses have shown that at the daily and weekly timescales individuals are well characterized by an activity space of repeatedly visited locations. How this activity space evolves in time, however, remains unexplored. Here we analyse high-resolution spatio-temporal traces from 850 individuals participating in a 24-month experiment. We find that, although activity spaces undergo considerable changes, the number of familiar locations an individual visits at any point in time is a conserved quantity. We show that this number is similar for different individuals, revealing a substantial homogeneity of the observed population. We point out that the observed fixed size of the activity space cannot be explained in terms of time constraints, and is therefore a distinctive property of human behavior.

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Recently developed information communication technologies, particularly the Internet, have affected how we, both as individuals and as a society, create, store, and recall information. Internet also provides us with a great opportunity to study memory using transactional large scale data, in a quantitative framework similar to the practice in statistical physics. In this project, we make use of online data by analysing viewership statistics of Wikipedia articles on aircraft crashes. We study the relation between recent events and past events and particularly focus on understanding memory triggering patterns. We devise a quantitative model that explains the flow of viewership from a current event to past events based on similarity in time, geography, topic, and the hyperlink structure of Wikipedia articles. We show that on average the secondary flow of attention to past events generated by such remembering processes is larger than the primary attention flow to the current event. We are the first to report these cascading effects.